Wanting to bring the benefits of technology into your earth science classroom? Dive into the treasure trove of satellite images that Landsat makes available everyday!

What is Landsat you may ask? “Landsat represents the world’s longest continuously acquired collection of space-based moderate-resolution land remote sensing data.” Meaning it is almost 40 years straight of images of earth from satellites. And the best part of it all…you can access them all! * to learn more about Landsat check out their About Landsat page

I think the easiest way to dive into these data are through the EarthExplorer interface. Here you can search the data first by location, either by knowing an exact address or place name OR by putting in a feature. Then you can select what timeframe you want to see the images from.

You can search locations:

around natural disasters before and after to see the changes.

that are getting warmer over time and look for other changes.

around your school from the first month of school each year going back as long as you have been there!

Enjoy bringing the technology of satellites and seeing things from space into your lessons!

https://earthexplorer.usgs.gov/

* If you are interested in seeing how scientists use satellite images in their work and how to connect your students with that, check out Dr. Ellyn Enderlin’s Polar-ICE Data Story “How and why are glaciers changing over time?” (there is a webinar recording, datasets, and lesson plans on the Polar-ICE Data Story page that accompany this resource).

Data visualizations are all around us and our students. And we incorporate data visualizations (e.g., graphs, maps, tables) into our teaching with data. But why do we visualize data in science? How can we create better visualizations? How do we develop our visualizations to tell a story with our data?

Join us each Wednesday as we dive into the world of data visualization and think about different aspects of creating more effective data visualizations.

Do we have to?

Our favorite question we so often receive when we ask students to do some of the work may just have a different meaning to it. Let’s take a step back and think if our students actually understand how to do what we are asking them to do when it comes to setting up and populating their own data tables.

The data tables are the key to organized data collection and are the precursor to setting you up to be successful in building your data visualizations (graphs or maps) from the data. But how often do we teach the “how and why” of data tables to our students?

Why the Parts of Data Tables

Data tables are after all just a matrix of boxes to put in information. One can willy-nilly place that information OR you can do it with some strategy to help you (and others) better understand your data.

So, there are rows and columns right? Why do we have each?

Columns – You can label the columns before you collect any data and then you get to be done with them 🙂 So what exactly should go in each column? These are your variables! What you are actually measuring. If you are running an experiment, then there is a column for your treatment and your control, and really anything else that you are measuring.

Rows – These are the data you collect. So if you have one measurement of data for each variable, then you will have one row. If you have more than one measurement (in other words replicates) then you have as many rows as you have measurements.

Another way to think about this is to remember that if you are calculating any sort of summary statistic of your data (e.g., mean, total counts, standard deviation) then that goes in the LAST row for each column (preferably identified with bold or italics to know it is not an actual measured data value). Therefore, having your students think about what they will want to take an average of can also help them think about organizing their data table.

How for Different Kinds of Data

Great, we got the basics of rows and columns but how does this vary based on the kind of data you are collecting?

We also have data that are not collected by a category but rather just have measurements for the x and y values. For example, you can measure the running speed and rate rate of each person in a class. Both of these variables are measured values, so these data are often referred to as “Interval-Ratio Data.”

We have data that are collected by categories that have no particular order. For example, you can compare apples then oranges and then bananas just the same as comparing bananas then apples and then oranges. There is no order to these categories, so these data are often referred to as “Nominal Data.”

We also have data that are collected by categories but the categories do have a particular order. For example, you can compare the average air temperature from Winter then Spring then Summer then Fall to get a sense of how it changes over the year, but it would not make sense to compare Fall then Spring then Summer then Winter. There is an order to these categories, so these data are often referred to as “Ordinal Data.”

Hopefully, looking at the small differences between the data tables across these three kinds of data helps you to see:

the similarities of how the columns and rows are set up regardless of the data type

how the data type influences the data table setup

how setting up the data table the right way puts you on the path to choosing and creating good data visualizations.

So, the next time you hear “do we have to?” take a moment to step back and think about if your students “know how to.” Have fun!

Data visualizations are all around us and our students. And we incorporate data visualizations (e.g., graphs, maps, tables) into our teaching with data. But why do we visualize data in science? How can we create better visualizations? How do we develop our visualizations to tell a story with our data?

Join us each Wednesday as we dive into the world of data visualization and think about different aspects of creating more effective data visualizations.

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